IEEE Sensors Journal最新文献

{"title":"Transforming Cardiovascular Care–Biosensors and Their Potential: A Review","authors":"Jegan Rajendran;Gymama Slaughter","doi":"10.1109/JSEN.2025.3559473","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3559473","url":null,"abstract":"The increasing global prevalence of cardiovascular diseases (CVDs) stresses the urgent need for cost effective, portable, and reliable biosensors to monitor cardiac health and detect disease biomarkers in real time. Recent advancements in the biosensor technology have harnessed flexible nanomaterials, chemical molecules, and integrated electronic circuits, enabling the development of compact and efficient medical devices. These innovations are driving the transition of biosensing techniques from laboratory settings to practical, real-world applications, including wearable and point-of-care (POC) devices. The seamless integration of biosensors with the human body allows for continuous, real-time cardiac monitoring, utilizing both invasive and noninvasive measurement techniques to detect critical cardiac biomarkers. Such devices enable early detection of CVDs and facilitate timely intervention, significantly improving patient outcomes. This review provides a comprehensive analysis of state-of-the-art biosensing methods for multimodal cardiac monitoring and diagnostics, highlighting recent progress in sensor development and integration with digital processors for cardiac biomarker screening. The hardware and software architectures involved in designing biosensors are also examined, with a focus on their application in tracking cardiac blood biomarkers and heartbeat signals. By evaluating current advancements, this review offers valuable insights for innovation in next-generation medical devices for early detection and continuous monitoring of cardiovascular conditions.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"16593-16613"},"PeriodicalIF":4.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microfluidic Paper-Based Portable Biochemistry Analyzer for Blood Parameter Sensing","authors":"Sangeeta Palekar;Bidipta Rana;Ayushh Khaire;Abhijit Awasthi;Minakshi Mahule;Jayu Kalambe","doi":"10.1109/JSEN.2025.3558549","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558549","url":null,"abstract":"A compact, portable, paper-based biochemistry analyzer has been developed for cost-effective, point-of-care blood parameter analysis. The device employs wax-printed paper-based micro-pads (<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>pads) coated with specific reagents to detect critical blood parameters. It analyzes blood serum for sugar, proteins, enzymes, electrolytes, and lipids, aiding in diagnosing, monitoring, and treating various medical conditions. A 3-D printed device is designed that houses a multispectral sensor (AS7341) interfaced with an ESP32 microcontroller measures absorbed light to analyze concentration in real time. To validate the performance of the developed device, various parameters, including albumin, creatinine, urea, triglycerides, total protein, and glucose, are tested. The <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>pads allow precise detection using a minimal sample volume of <inline-formula> <tex-math>$100~mu $ </tex-math></inline-formula>L, making it especially suitable for low-resource settings. Additionally, the linear range for respective analytes is: albumin up to 8 g/dL, creatinine up to 20 mg/dL, triglycerides up to 1000 mg/dL, total protein up to 15 g/dL, and glucose up to 600 mg/dL, with corresponding limits of detection (LODs) at 0.134 g/dL, 0.087 mg/dL, 0.055 mg/dL, 0.094 g/dL, and 0.095 mg/dL, respectively. The portable design, combined with its ease of use and low-cost components, makes this analyzer a practical solution for point-of-care diagnostics, particularly in remote or underserved regions, where conventional laboratory equipment is impractical due to size, cost, and operational requirements. By overcoming the limitations of traditional bulky analyzers, this device has the potential to significantly enhance accessibility to biochemical diagnostics in healthcare.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18155-18162"},"PeriodicalIF":4.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCAGN: Multilayer Coupled Attention Graph Networks for Composites’ Damage Detection","authors":"Jitong Ma;Sen Zhao;Zhengyan Yang;Wenqiang Bao;Jie Wang;Zhanjun Wu","doi":"10.1109/JSEN.2025.3558994","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558994","url":null,"abstract":"Damage detection in carbon-fiber-reinforced polymer (CFRP) composites is essential for maintaining structural integrity and preventing catastrophic accidents. Recently, machine-learning-based damage detection methods have garnered increasing attention. However, existing approaches often face challenges in extracting relational information about damage, both within guided wave paths and between damage samples. These limitations result in insufficient detection stability under varying training sample conditions and increase the risk of model overfitting when relying solely on high-dimensional information extracted from guided wave signals. To address these challenges, this article proposes a multilayer graph network with a coupled attention mechanism based on feature fusion. In the proposed method, by encoding sensor coordinates and fusing guided wave signal features, positional information can be integrated into the guided wave signals. A multilayer graph network is further used to extract signal features, while the interlayer coupled attention mechanism and multilayer graph construction fully explore the relational information associated with the damage. Experimental datasets collected from real CFRP composites demonstrate the effectiveness of the proposed method. Comparative results show that the multilayer graph neural network (GNN) achieves superior stability and accuracy, even with limited training samples and noisy training samples. These findings highlight the robustness and practicality of the proposed approach for damage detection in CFRP composites.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18553-18564"},"PeriodicalIF":4.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Rule-Based Architecture for GNSS Jamming Signal Classification","authors":"Sindhusha Jeeru;Lei Jiao;Per-Arne Andersen;Ole-Christoffer Granmo","doi":"10.1109/JSEN.2025.3558966","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558966","url":null,"abstract":"Jamming is a fatal threat to a global navigation satellite system (GNSS), and an efficient anti-jamming system relies on successful classification and identification of jamming types to respond effectively. The existing solutions suffer either from poor accuracy or lack of interpretability, and they are prone to learning simple statistical correlations rather than more fundamental and general relationships. In this study, we propose a novel approach to classify GNSS jamming signals as intentional or unintentional. The approach introduces a new standard deviation-based denoising method, which makes it possible to use the logical rule-based architecture of the convolutional Tsetlin machine (CTM) for interpretable jamming signal analysis. CTM is a recently developed algorithm that solves complex classification problems using conjunctive propositional formulas through a team of Tsetlin automata (TA). Unlike traditional opaque models based on deep learning, our approach goes beyond classification and provides a human-level interpretation of features. This interpretation capability allows a deeper comprehension of the characteristics and underlying patterns of the jamming signals, significantly easing the decision-making process. Furthermore, the CTM approach is also evaluated with different Booleanization techniques. Through experiments, we show that the proposed approach with CTM achieves an F1-score of 98.7%, on the collected dataset, which is superior to the state-of-the-art.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17942-17959"},"PeriodicalIF":4.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Human Action Recognition With Fine-Grained Spatiotemporal Feature Extraction From Millimeter-Wave Point Clouds","authors":"Zhuo Chang;Shilong Lou","doi":"10.1109/JSEN.2025.3558856","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558856","url":null,"abstract":"Human activity recognition (HAR) based on millimeter-wave (mmWave) radar point clouds has attracted much attention due to its privacy protection properties. The point cloud sequence generated by mmWave radar contains the appearance and motion features of objects and contains rich spatiotemporal information. However, due to the sparsity, nonuniformity, and noise interference of mmWave point clouds, existing methods had difficulty in effectively extracting fine-grained spatiotemporal features from point cloud sequences. To address these problems, we propose a new HAR system for mmWave radar point clouds that can effectively extract fine-grained spatiotemporal features in point cloud sequences and significantly reduce computational overhead. Our system first preprocesses the raw point cloud to generate a clean and standardized point cloud. Then, it uses shared weight TF-Net and PointNet++ to extract features and centroid coordinates for each point cloud frame and inputs them into our designed ST-Transformer layer. This layer decouples and encodes the spatiotemporal structure of the centroid coordinates to capture fine-grained spatiotemporal information. Finally, a lightweight neural network based on a multilayer perceptron (MLP) performs classification. The whole process avoids voxelization, reducing memory requirements and computational complexity. We conduct extensive experiments on RadHAR and Pantomime datasets to evaluate the effectiveness of the proposed system, achieving average recognition accuracies of 98.8% and 99.1%, respectively, which is detailed in the Experimental Results section.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17919-17930"},"PeriodicalIF":4.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vector Magnetic Field Sensor Based on Magneto-Sensitive Functionalized Three-Core Fiber Structure","authors":"Ronghui Xu;Ruifeng Liu;Yuxin Wei;Chenglong Jiang;Jingyu Lai;Ming Chen;Shiliang Qu;Libo Yuan","doi":"10.1109/JSEN.2025.3554303","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3554303","url":null,"abstract":"We have proposed and experimentally demonstrated a vector magnetic field sensor based on a magneto-sensitive functionalized three-core fiber (TCF) structure. The fiber-optic magnetic field sensor adopts a sandwich-like magnetofluid encapsulated single-mode fiber (SMF)-TCF-SMF structure. Owing to the noncircular symmetric structure, thereby enabling the fiber sensing structure exhibits response to environmental magnetic field strength and direction. The experimental results shows that the maximum strength sensitivity and direction sensitivity are −1.23 nm/mT and 0.73 nm/°, respectively, within the range of 35–44 mT when the TCF length is 10 mm. The proposed magnetic field sensor offers several advantages, including simple structure, ease of fabrication, and high sensitivity.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17043-17054"},"PeriodicalIF":4.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Differential Microwave Sensor Based on Modified High-Sensitivity Substrate-Integrated Waveguide (SIW) for Detecting Glucose Concentration in Aqueous Solution","authors":"Hao Xie;Wen-Jing Wu;Wen-Sheng Zhao;Wensong Wang","doi":"10.1109/JSEN.2025.3558517","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558517","url":null,"abstract":"A differential microwave sensor system based on a modified high-sensitivity substrate-integrated waveguide (SIW) for detecting glucose concentration in an aqueous solution is proposed in this article. An RF signal generator, a broadband 3-dB directional coupler, two modified SIWs, and a gain/phase detector constitute the proposed microwave sensor system, wherein, the broadband 3-dB directional coupler is a second-order 3-dB directional coupler, whose relative bandwidth is 53.42% higher than the single-order 3-dB directional coupler. Two complementary split-ring resonators (CSRRs) are carved onto the upper surface of SIW, and the meander slots are embedded into CSRRs to confine more electrical field. Besides, the etched cross-shaped grooves are added to the area between the two meander slots to concentrate electrical field density. In the proposed microwave sensor, the through and coupled ports of the coupler are each connected to an SIW. Meanwhile, one is regarded as a reference, and the other for testing, both being SIWs. The two output ports of the SIWs are connected to the two input ports of the gain/phase detector. In the system, the signal output port of the RF generator is connected to the input port of the coupler. When the RF generator outputs an oscillation signal, the detector can separately convert the differences in amplitude and phase of these two input signals into a respective output dc voltage. The output dc voltages can be used to detect glucose concentration, as changes in glucose concentration would be reflected in various of the dc voltages. In measurement, the proposed microwave sensor has average sensitivities of about 0.698 and 1.014 mV/(mg/dL) for the two output dc voltages. Furthermore, the dc voltage amplifiers are added to the proposed sensor system, and the average sensitivities become 3.04 and 4.21 mV/(mg/dL) for the two channels. The proposed microwave sensor incorporates differential detection to diminish the impact of external environment factors, and the added active RF gain/phase detector can abandon the utilization of a vector network analyzer (VNA) and lower the cost. With these advantages, the proposed microwave sensor is an excellent candidate in the region of characterizing liquid samples.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"16998-17010"},"PeriodicalIF":4.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CTGANN: Channel-Mixed and Temporal Gated Attention Neural Network for GNSS/INS Compensation by Predicting Pseudo-Velocity During GNSS Outages","authors":"Han Zhang;Zhen Liu;Qianxin Wang;Zengke Li;Xu Wu","doi":"10.1109/JSEN.2025.3558968","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558968","url":null,"abstract":"The global navigation satellite system (GNSS) and the inertial navigation system (INS) integrated navigation system provide continuous and high-accuracy positioning; however, positioning accuracy deteriorates during GNSS outages due to the accumulation of INS errors. To address this challenge, we propose an efficient and novel model named channel-mixed and temporal gated attention neural network (CTGANN) to compensate for INS errors during GNSS outages by predicting pseudo-velocity. Compared to pseudo-position compensation, pseudo-velocity prediction effectively mitigates the accumulation of model prediction errors, resulting in a more stable and reliable solution during extended GNSS outages. When GNSS signals are available, CTGANN learns the complex nonlinear relationship between INS and GNSS measurements. During GNSS unavailability, CTGANN generates pseudo-velocity GNSS measurements to compensate, thereby effectively suppressing the divergence of positioning errors. CTGANN leverages the time mixing layer to effectively capture the underlying temporal dependency patterns in the data, while the channel-mixing layer emphasizes critical features and reduces redundant information. The proposed model’s performance was evaluated through field tests, and results show that CTGANN significantly improves GNSS/INS positioning accuracy during GNSS outages, outperforming other models.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17931-17941"},"PeriodicalIF":4.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphone Platform for Multisignal Parallel Detection, Processing, and Visualization in Fluorescence Biosensing","authors":"Weihe Zhan;Qingfubo Geng;Baole Wang;Yian Liu;Zhaoxin Geng","doi":"10.1109/JSEN.2025.3558238","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3558238","url":null,"abstract":"The fluorescence sensing method is widely used in biochemical sensing. With the development of hardware structures and software systems in smartphones, this method has gradually evolved from large laboratory equipment to smartphones. However, current methods mainly rely on single signal processing and provide few detection schemes, requiring greater flexibility and efficiency. To overcome these challenges, a new smartphone platform is proposed for signal processing in fluorescence biosensing experiments. The platform supports multisignal parallel processing and precise target localization by integrating multiple image preprocessing algorithms and a Hough transform-assisted grayscale distribution algorithm. In addition, the platform provides multiple methods for sampling and concentration curve fitting, ensuring the flexibility and verifiability of experimental design. The experimental results demonstrate that the localization algorithm achieves a hit rate of 99.48%, with an average deviation distance of only 3.595 pixels. The minimum mean square error (mse) between the average pixel value of the target signal area calculated by the platform and the actual pixel value is only 3.158, and the minimum mean absolute error (MAE) is only 1.360. Validation experiments further confirm that the sensitivity and accuracy of the platform meet the basic requirements for fluorescence sensing detection. This platform has broad applicability in biomedical engineering and point-of-care testing (POCT), improving portable diagnostic tools and paving the way for home biochemical sensing applications, such as early cancer screening and cardiovascular disease prevention.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"18312-18322"},"PeriodicalIF":4.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alterations in Phase Synchronization During Epileptic Seizures: A Frequency-Based Multilayer EEG Network Analysis","authors":"Dian Zhang;Jiuchuan Jiang;Yupeng Wang;John Q. Gan;Haixian Wang","doi":"10.1109/JSEN.2025.3557802","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3557802","url":null,"abstract":"Epilepsy is a common neurological disorder, and electroencephalogram (EEG) is widely used for its detection and analysis. EEG-based functional brain networks play a key role in revealing epileptic seizure patterns. Multilayer networks, which can capture multiple types of interactions, have shown great potential in this domain. However, previous analysis frameworks often simplified multilayer networks into single-layer subnetworks, ignoring the multilayer structure, or adopted a multiplex analysis framework, ignoring the importance of interlayer connections. In this study, we proposed a multilevel analysis framework for frequency-based multilayer networks, ranging from supra-adjacency matrix, subnetwork, node, layer, and global levels. We applied this framework to the Temple University Hospital EEG Seizure Corpus (TUSZ) to investigate the phase synchronization changes between the interictal (between seizures) and ictal (during seizures) phases of epileptic seizures. Compared to the interictal phase, the ictal phase showed increased local connectivity, decreased overall network connectivity, and a shift from random to regular network organization. Meanwhile, cross-frequency coupling (CFC) analysis suggested that <inline-formula> <tex-math>$delta $ </tex-math></inline-formula>–<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula> (frontal and parietal-occipital regions) and <inline-formula> <tex-math>$theta $ </tex-math></inline-formula>–<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula> (frontal and right frontal-temporal regions) bands are potentially associated with seizure propagation or termination. These findings provide insights for future analysis of CFC in seizures and intervention strategies by interpreting seizure propagation networks. In conclusion, our framework enables efficient multilayer network analysis in epilepsy, facilitating the identification of key frequency bands and brain regions in CFC analysis.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 10","pages":"17905-17918"},"PeriodicalIF":4.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}